[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"1bf1j0q":3,"kyq71e":73},{"common":4,"navPages":22,"productGroups":33,"seo":66,"strategies":72},{"_id":5,"_type":5,"cta_label":6,"cta_label_impact":7,"email":8,"socials":9},"settings_common","let’s talk","read impact studies",null,[10,14,18],{"_key":11,"label":12,"type":12,"url":13},"668f9a890fd5","linkedin","https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fsphere-energy\u002F",{"_key":15,"label":16,"type":16,"url":17},"5a142892073b","medium","https:\u002F\u002Fmedium.com\u002F@lutz.lukas88",{"_key":19,"label":20,"type":20,"url":21},"f97a157c8bf7","youtube","https:\u002F\u002Fwww.youtube.com\u002F@SphereEnergy",[23,27,30],{"_id":24,"slug":25,"title":26},"8b8dc55d-3fef-48a3-8152-48bcedea3bf6","studies ","impact studies",{"_id":28,"slug":29,"title":29},"15dbbb59-c8d8-46d2-bd7c-722ffe763ccf","about",{"_id":31,"slug":32,"title":32},"d529968f-1fdb-40a0-8c9d-f6e1ff64ef9f","vetta",[34,50],{"_id":35,"products":36,"title":49},"865c3cc9-1bd1-4eb7-9b45-997456c72b35",[37,41,45],{"_id":38,"slug":39,"title":40},"c02a84c3-1cd4-4ccf-881d-ec8ebd92a8b5","battery-life-prediction","Battery life prediction",{"_id":42,"slug":43,"title":44},"ff6446f7-e857-4ac5-9361-ef70a82e4e92","soh-estimation","SOH Estimation",{"_id":46,"slug":47,"title":48},"c1e2a74c-c4e9-4b06-80cb-19c7a9a12867","parametrisation","Parametrisation","Battery AI",{"_id":51,"products":52,"title":65},"f62d95db-3849-497c-aced-e4224dc82287",[53,57,61],{"_id":54,"slug":55,"title":56},"26365138-af07-43a8-b1f5-8b8539034357","requirements-processing","Requirements processing",{"_id":58,"slug":59,"title":60},"8a92b84d-7114-43dd-97fe-7965c6bfcaac","cad-intelligence","CAD Intelligence",{"_id":62,"slug":63,"title":64},"d5321be1-6a2e-4414-8196-88e25567ce30","crash-simulation","Crash Simulation","Engineering AI",{"description":67,"og_description":68,"og_image":69,"og_title":70,"title":71},"Sphere builds Vetta, an AI operating system for physical product development. It unifies engineering data, orchestrates AI models and supports workflows across the entire product lifecycle.","Vetta is the AI operating system for physical product development, helping engineering teams connect data, models and workflows across the full development lifecycle.","https:\u002F\u002Fcdn.sanity.io\u002Fimages\u002Fowk14ohd\u002Fproduction\u002Fa750d9adc1a676399bfcb13d2eee377f5583c9de-1200x630.jpg","Sphere | AI Operating System for Physical Product Development","Sphere Intelligence",[],{"_id":46,"_type":74,"blocks":75,"seo":8,"slug":47,"title":48},"product",[76,120,134,155,238,257,358,419],{"_key":77,"_type":78,"active":79,"description":80,"image":92,"keypoints":101,"title":119},"c1n0wo20iz7","productIntroBlock",true,[81],{"_key":82,"_type":83,"children":84,"markDefs":90,"style":91},"7f432f21cb6b","block",[85],{"_key":86,"_type":87,"marks":88,"text":89},"c83414c00994","span",[],"Building accurate ECM and P2D models is critical for advanced battery design — but manual parameterization remains a major bottleneck. Teams spend weeks curve-fitting parameters across temperatures, SOCs, and C-rates by hand.",[],"normal",{"image":93,"imageMobile":98},{"_type":94,"asset":95},"image",{"_ref":96,"_type":97},"image-b0d808f8d68afa193d63eaf4a304720c3ac62b89-1845x1965-png","reference",{"_type":94,"asset":99},{"_ref":100,"_type":97},"image-f6748766b6f73f6d134f457c12c352ab5a19b961-686x406-jpg",[102,107,111,115],{"_key":103,"_type":104,"keypoint":105,"title":106},"QYI18RjWMXpR","keypoint","4,000+","Real-life cycling tests",{"_key":108,"_type":104,"keypoint":109,"title":110},"3UJCDKNloJGy",">10M","Synthetic material configurations",{"_key":112,"_type":104,"keypoint":113,"title":114},"1UTXIwEUFM2V","\u003C 2%","Error for extracted KPIs",{"_key":116,"_type":104,"keypoint":117,"title":118},"fFphrZi9EdKn",">90%","Parameterization time reduction","From weeks of manual curve-fitting to automated physical parameterization",{"_key":121,"_type":122,"active":79,"description":123,"subtitle":132,"title":133},"rrofrw8snl","productHeroBlock",[124],{"_key":125,"_type":83,"children":126,"markDefs":131,"style":91},"biffjs8fmc70",[127],{"_key":128,"_type":87,"marks":129,"text":130},"ly3d74z96ng0",[],"Testing teams currently spend weeks analyzing complex charge\u002Fdischarge data across fluctuating temperatures and operational profiles, introducing human error and severely delaying time-to-market.",[],"This is not an engineering failure. It is a process failure.","Your best engineers are losing weeks on manual curve-fitting routines",{"_key":135,"_type":136,"active":79,"description":137,"tagLabel":32,"title":146},"0ehbx41ot2v","productPatternBlock",[138],{"_key":139,"_type":83,"children":140,"markDefs":145,"style":91},"g82amvmq5f60",[141],{"_key":142,"_type":87,"marks":143,"text":144},"c0l10i6h5390",[],"vetta integrates every raw charge\u002Fdischarge test, cycling dataset, and synthetic material configuration into one automated physical parameterization engine. Engineering teams can instantly calibrate ECM and P2D models, simulate complex operational profiles, and completely eliminate weeks of manual curve-fitting routines.",[],[147],{"_key":148,"_type":83,"children":149,"markDefs":154,"style":91},"q11a5csnizk0",[150],{"_key":151,"_type":87,"marks":152,"text":153},"f6akdkfa0ia0",[],"One engine. All physics-based models.",[],{"_key":156,"_type":157,"active":79,"cards":158,"theme":220,"title":221},"ak5hizjelah","productCardsBlock",[159,180,200],{"_key":160,"_type":161,"description":162,"title":171},"RXKVh7JPZ8Ll","productCard",[163],{"_key":164,"_type":83,"children":165,"markDefs":170,"style":91},"d93d59687082",[166],{"_key":167,"_type":87,"marks":168,"text":169},"865ba02699be",[],"Trained on 4,000+ cycling tests and >10M material configurations, the platform learns which parameters to optimize for ECM and P2D models directly from your data.",[],[172],{"_key":173,"_type":83,"children":174,"markDefs":179,"style":91},"0y4gik7gkipd",[175],{"_key":176,"_type":87,"marks":177,"text":178},"5qr4w8aucbj0",[],"Al model support",[],{"_key":181,"_type":161,"description":182,"title":191},"OwyyRt2E1s1Z",[183],{"_key":184,"_type":83,"children":185,"markDefs":190,"style":91},"fb9ca2b2c7a7",[186],{"_key":187,"_type":87,"marks":188,"text":189},"bed5d961c643",[],"Minimal adaptation tailored to your cell chemistries and test protocols — building unique IP and deep battery intelligence on high-performance cloud infrastructure.",[],[192],{"_key":193,"_type":83,"children":194,"markDefs":199,"style":91},"21hvsetpspm0",[195],{"_key":196,"_type":87,"marks":197,"text":198},"q0u8nx677g00",[],"optimization",[],{"_key":201,"_type":161,"description":202,"title":211},"1raMnDdxBm14",[203],{"_key":204,"_type":83,"children":205,"markDefs":210,"style":91},"c9b10b4681a3",[206],{"_key":207,"_type":87,"marks":208,"text":209},"4514227220e7",[],"Simulate any test conditions using pre-built measurement templates or an integrated LLM that generates operating profiles automatically.",[],[212],{"_key":213,"_type":83,"children":214,"markDefs":219,"style":91},"x9a52gih8c80",[215],{"_key":216,"_type":87,"marks":217,"text":218},"17d9ylk2f6p0",[],"simulation",[],"dark",[222,230],{"_key":223,"_type":83,"children":224,"markDefs":229,"style":91},"iniawsv7igc0",[225],{"_key":226,"_type":87,"marks":227,"text":228},"krlc17y1s6t0",[],"Three steps from cell data",[],{"_key":231,"_type":83,"children":232,"markDefs":237,"style":91},"fqigcl4btk90",[233],{"_key":234,"_type":87,"marks":235,"text":236},"n21ulewtgl00",[],"to physical battery intelligence",[],{"_key":239,"_type":240,"active":79,"imageAlt":241},"ldnfjppfnbb","productImageBlock",{"_type":242,"image":243,"imageMobile":246},"imageAlt",{"_type":94,"asset":244},{"_ref":245,"_type":97},"image-9ba68b339f17618058689dc2e6e0aedce5e580c2-4320x1563-png",{"_type":94,"_upload":247,"asset":255},{"createdAt":248,"file":249,"previewImage":252,"progress":253,"updatedAt":254},"2026-07-14T11:52:59.334Z",{"name":250,"type":251},"product_image_mob.png","image\u002Fpng","data:image\u002Fjpeg;base64,\u002F9j\u002F4AAQSkZJRgABAQAAAQABAAD\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\u002F2wBDAFA3PEY8MlBGQUZaVVBfeMiCeG5uePWvuZHI\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F2wBDAVVaWnhpeOuCguv\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002F\u002FwAARCAB2AFgDASIAAhEBAxEB\u002F8QAFwABAQEBAAAAAAAAAAAAAAAAAAECBP\u002FEACMQAQEBAAEDBAIDAAAAAAAAAAABEVESITECQWGRMoFScaH\u002FxAAUAQEAAAAAAAAAAAAAAAAAAAAA\u002F8QAFBEBAAAAAAAAAAAAAAAAAAAAAP\u002FaAAwDAQACEQMRAD8A5gi9vkEFAQUBBQEFAQACN9Uz8J53WFBrq83pmJLJ7IATQAAAAAQACLf7SNZMBP2GZQADtyAAAfs7aXOQQACNd\u002FlmNbPn7Ay8VMvBptAy8GXg00DLwZYaAZeDLm52NAQABcsRQJ6bfEMptATBQEFAQUBAAFy8Iu3kAXq9XNS+q3zdABeq8T6BAXZ\u002FGAgduC57AgACoAoigB24v2TJd8zgALnft\u002FpcyZMvv38gCAAAAAAAAAAAAAAAP\u002F\u002FZ",100,"2026-07-14T11:53:01.466Z",{"_ref":256,"_type":97},"image-e53e9a631274159ee24bc9569789c251bf14d21c-750x1012-png",{"_key":258,"_type":157,"active":79,"cards":259,"subtitle":340,"title":341},"sxhz161mrks",[260,280,300,320],{"_key":261,"_type":161,"description":262,"title":271},"DnXNROYkmqCx",[263],{"_key":264,"_type":83,"children":265,"markDefs":270,"style":91},"5d30248e3026",[266],{"_key":267,"_type":87,"marks":268,"text":269},"0ea4605a3fc3",[],"Extract parameters from raw test data to evaluate how physical modifications — electrode thickness, porosity, active material composition — impact cell performance across operating conditions.",[],[272],{"_key":273,"_type":83,"children":274,"markDefs":279,"style":91},"jmgcs1o0b8p0",[275],{"_key":276,"_type":87,"marks":277,"text":278},"vck0mdjo5v00",[],"rapid cell material screening & optimization",[],{"_key":281,"_type":161,"description":282,"title":291},"V6ln3wBCS0wK",[283],{"_key":284,"_type":83,"children":285,"markDefs":290,"style":91},"93a7fc2e09ad",[286],{"_key":287,"_type":87,"marks":288,"text":289},"da6a85a0a2a1",[],"Automatically optimize ECM models across temperature, C-rate, SOC, and SOH matrices. Generates production-ready assets for BMS firmware or thermal validation during fast charge profiles.",[],[292],{"_key":293,"_type":83,"children":294,"markDefs":299,"style":91},"8ybxoc9a92b0",[295],{"_key":296,"_type":87,"marks":297,"text":298},"f477s9d20if0",[],"cross-context cell models",[],{"_key":301,"_type":161,"description":302,"title":311},"KzF86eKOCFsA",[303],{"_key":304,"_type":83,"children":305,"markDefs":310,"style":91},"9ed464c1eaf7",[306],{"_key":307,"_type":87,"marks":308,"text":309},"cbaebdeb46f4",[],"Isolate specific degradation mechanisms early in validation — pinpointing exactly how and why a cell fails under thermal stress or extreme SOC conditions.",[],[312],{"_key":313,"_type":83,"children":314,"markDefs":319,"style":91},"m8fhco9fes00",[315],{"_key":316,"_type":87,"marks":317,"text":318},"651wvd3f3o00",[],"advanced aging and degradation analysis",[],{"_key":321,"_type":161,"description":322,"title":331},"Cc0WGm0bMpFn",[323],{"_key":324,"_type":83,"children":325,"markDefs":330,"style":91},"90c27f61b19c",[326],{"_key":327,"_type":87,"marks":328,"text":329},"b0a702f172ce",[],"Scale and accelerate internal calibration pipelines via high-velocity cloud infrastructure, deployed through secure European providers like StackIT.",[],[332],{"_key":333,"_type":83,"children":334,"markDefs":339,"style":91},"00k9vbnt1qsb",[335],{"_key":336,"_type":87,"marks":337,"text":338},"skadzkzle6f0",[],"cloud computing for parallelizable optimization",[],"use cases",[342,350],{"_key":343,"_type":83,"children":344,"markDefs":349,"style":91},"kd9gmf4to800",[345],{"_key":346,"_type":87,"marks":347,"text":348},"jgo8df705980",[],"What your team",[],{"_key":351,"_type":83,"children":352,"markDefs":357,"style":91},"kykaikuvouf0",[353],{"_key":354,"_type":87,"marks":355,"text":356},"32xmst9hnf60",[],"can do with it",[],{"_key":359,"_type":360,"active":79,"cards":361,"subtitle":401,"tagLabel":32,"title":402},"dlt0ol325g","productDeployBlock",[362,376,388],{"_key":363,"_type":364,"description":365,"title":374,"titleCircle":375},"233f07d0cb7e","deployCard",[366],{"_key":367,"_type":83,"children":368,"markDefs":373,"style":91},"4aa4c4ca05f1",[369],{"_key":370,"_type":87,"marks":371,"text":372},"964151cbdbd6",[],"We test the automated parameterization engine directly with a sample of your laboratory data, establishing flexible configuration arrays for experimental fitting with either ECM or P2D models.",[],"integrate into your data pipeline","pipeline",{"_key":377,"_type":364,"description":378,"title":387,"titleCircle":387},"62dad037c44d",[379],{"_key":380,"_type":83,"children":381,"markDefs":386,"style":91},"7d0aa023f19a",[382],{"_key":383,"_type":87,"marks":384,"text":385},"320157cf28dc",[],"Parameterization workflows customized to your business goals. All IP stays with you, running on-premises or in a private cloud.",[],"integration",{"_key":389,"_type":364,"description":390,"title":399,"titleCircle":400},"2f77fa3f3fef",[391],{"_key":392,"_type":83,"children":393,"markDefs":398,"style":91},"b3d991743093",[394],{"_key":395,"_type":87,"marks":396,"text":397},"146acf2f236a",[],"Full infrastructure deployment inside your private cloud or on-premises, with seamless integration across your core platform tools.",[],"deployment & license","deploy","how we deploy",[403,411],{"_key":404,"_type":83,"children":405,"markDefs":410,"style":91},"o4if4rr7gnk0",[406],{"_key":407,"_type":87,"marks":408,"text":409},"x00fi5wysv00",[],"Not a pilot. A permanent",[],{"_key":412,"_type":83,"children":413,"markDefs":418,"style":91},"uuran6a89a00",[414],{"_key":415,"_type":87,"marks":416,"text":417},"d0xkra86uop0",[],"operating relationship.",[],{"_key":420,"_type":421,"active":79,"buttonLabel":422,"description":423,"icons":440,"title":486},"biwz56c3g26","productCtaBlock","run a proof of concept",[424,432],{"_key":425,"_type":83,"children":426,"markDefs":431,"style":91},"h8sjz4lr5fq0",[427],{"_key":428,"_type":87,"marks":429,"text":430},"e21e399cs7w0",[],"One unified physical parameterization platform trained on extensive material configurations, deployed",[],{"_key":433,"_type":83,"children":434,"markDefs":439,"style":91},"kqrs5qnm4m90",[435],{"_key":436,"_type":87,"marks":437,"text":438},"57xmp3v3etm0",[],"securely inside your own infrastructure.",[],[441,453,464,475],{"_key":442,"_type":443,"title":444},"fwRWwRP73Vuz","ctaIcon",[445],{"_key":446,"_type":83,"children":447,"markDefs":452,"style":91},"vaz8ctry6y00",[448],{"_key":449,"_type":87,"marks":450,"text":451},"7pfdh8pd8yh0",[],"80% OpEx savings",[],{"_key":454,"_type":443,"title":455},"qqZApK8aHLU2",[456],{"_key":457,"_type":83,"children":458,"markDefs":463,"style":91},"yu06d6hvts00",[459],{"_key":460,"_type":87,"marks":461,"text":462},"j50b655bpw00",[],"90% efficiency boost",[],{"_key":465,"_type":443,"title":466},"bhtSWeGl7gWB",[467],{"_key":468,"_type":83,"children":469,"markDefs":474,"style":91},"2edb220dd2e1",[470],{"_key":471,"_type":87,"marks":472,"text":473},"2b2763ed61bd",[],"minutes to results",[],{"_key":476,"_type":443,"title":477},"a75daruJKir8",[478],{"_key":479,"_type":83,"children":480,"markDefs":485,"style":91},"d53c967250bf",[481],{"_key":482,"_type":87,"marks":483,"text":484},"5e12ffb6a955",[],"unified battery intelligence",[],[487],{"_key":488,"_type":83,"children":489,"markDefs":494,"style":91},"5bisqy4ynnl0",[490],{"_key":491,"_type":87,"marks":492,"text":493},"wheyzoimepo0",[],"Payback in months, not years",[]]